1. Field of the Invention
The present invention relates to an image noise reduction method, and more particularly, to an image noise reduction method by determining similarity between a center pixel and adjacent pixels to reduce image noise.
2. Description of the Prior Art
In an era of multimedia communication, image data is playing an important role. But no images are absolutely perfect no matter how good a camera is, since images are interfered by the presence of noise. The principal sources of noise in digital images arise during image acquisition (digitization) and/or transmission. The performance of imaging sensors is affected by a variety of factors, such as environmental conditions during image acquisition, and by the quality of the sensing elements themselves. For instance, in acquiring images with a CCD camera, luminosity and sensor temperature are major factors affecting the amount of noise in the generated images. Images are corrupted during transmission principally due to interference in channels used for transmission. For example, an image transmitted by a wireless network might be disturbed as a result of lightning or other atmospheric charged particles.
Filtering a digital image is one necessary part in image processing and is used for reducing noise when protecting image details. For example, any noise in images will result in serious errors due to many applications being based on operands drawn out from applications for calculating images. Therefore, filtering methods for reducing noise are desired not only to improve the visual quality, but also to improve the performance of subsequent processing tasks such as coding, analysis cutting, identification, or interpretation.
In digital images, image pixels are usually interfered by impulse noise due to wrong image acquisition equipments, ill image acquisition conditions, or errors in image transmission. The median filter provides perfect noise reduction capability to certain kinds of random noise and generates clearer images than linear smooth filters with the same size. Impulse noises are discovered easily by human eyes and cause serious mistakes in image processing applications. Hence, impulse noises are used for front end processing in some image processing system, such as image quantification. A best impulse noise filter must have capacity to smooth non-similar pixels in identical areas, to keep edge information, and not to change any natural image information. Different impulse noise reduction algorithms are already disclosed in the past years, their purposes are to filter impulse noise and to keep image details at the same time. Some typical non-linear filters, such as median filters and weighted median filters are used for reducing almost all impulse noises and keeping almost all image details.
Applications of reducing image noise are already disclosed by some scholars. For example, a differential rank impulse detector (DRID) is provided in the prior art for detecting impulse noise effectively. In a motion window, the difference between the arrangement sequence of impulse noise and the arrangement sequence of a center pixel is very large. The median values in different sequences always lie in the middle, but the median value of impulse noises lie near two extremities. A simple impulse noise detector can be obtained for this reason, its conception is comparing the location of desired pixel with a threshold and can be expressed in the following equation:(R(Xi,j)≦s)(R(Xi,j))≧N−s+1;
wherein, Xi,j is a center pixel of a motion window, R(Xi,j) is a rank after sorting, N is a pixel amount of pixels in the motion window, and s is a threshold value. It is easy to determine noise interference and to get great effect through this method, but there are many erroneous judgments and whether a pixel is interfered by noise or not is not guaranteed. A pixel is regarded as noise if it is not interfered by noise and not sorted near two extremities. In order to overcome this problem, not only the sorting sequence but also the gray scale value should be considered. The algorithm can be expressed as another equation:(R(Xi,j)≦s)(R(Xi,j)≧(N−s+1))^(di,j≧θ);
wherein, di,j can be expressed as:
      d          i      ,      j        ≡      {                                                                                                                            x                                          i                      ,                      j                                                        -                                      Var                    ⁡                                          [                                                                        R                          ⁡                                                      (                                                          x                                                              i                                ,                                j                                                                                      )                                                                          -                        1                                            ]                                                                      ⌉                            ,                                                                          if                ⁢                                                                  ⁢                                  R                  ⁡                                      (                                          x                                              i                        ,                        j                                                              )                                                              >                              MED                                  i                  ,                  j                                                                                                                                                                              x                                          i                      ,                      j                                                        -                                      Var                    ⁡                                          [                                                                        R                          ⁡                                                      (                                                          x                                                              i                                ,                                j                                                                                      )                                                                          +                        1                                            ]                                                                                                  ,                                                                          if                ⁢                                                                  ⁢                                  R                  ⁡                                      (                                          x                                              i                        ,                        j                                                              )                                                              <                              MED                                  i                  ,                  j                                                                                                        0              ,                                            else                              ;      
Var(k) is the gray scale value of a sorting K. The detector provides an effective, fast, with non-smooth image, and can be applied in any other filter method based on comparing the location of pixels with an absolute value.
In another reference document, a conditional signal-adaptive median filter (CSAM) is provided in the prior art, which is a median filter based on judgments. The filter is consisted of two primary functions-necessary conditions for determination and method for filtering noise. The first function is used for determining whether noise exists in a motion window or not, and the second function is used for smoothing pixel value of noise. The algorithm is expressed as the following:
Step 1: Calculating Upper Limits and Lower Limits in Identical Areas.
Step 2: Detecting Impulse Noises.
In a 3×3 motion window, let a center pixel be X0, 8 neighbors be zi|i=18, ch be a pixel amount of pixels that is identical to the center pixel X0 in the 8 neighbors, and ci be a pixel amount of pixels that is not identical to the center pixel X0 in the 8 neighbors. The center pixel X0 is determined to be noise if the value ch is greater than the value ci, and the center pixel X0 is determined to be a noise candidate if the value ch is not greater than the value ci.
Step 3: Refining the Selected Impulse Noise.
Different filtering is utilized to remove pixels not interfered by noise from the noise candidate to lower error detections. Those erroneous detected pixels lie most near edge and image details. Dividing those pixels into two groups: one group is similar to the center pixel, and the other group is not similar to the center pixel. The center pixel is determined as signal and is removed from the noise candidate if the pixel amount of pixels that are similar to the center pixel is greater than the pixel amount of pixels that are not similar to the center pixel. The step is executed continuously till the number of the noise candidate is not decreased anymore.
Step 4: Using a Median Filter to Reduce Noise.
A 3×3 median filter is used to reduce noise in a 3×3 motion window if the pixel amount of pixels that are similar to the center pixel is smaller than 3. Otherwise, a 5×5 median filter is used to reduce noise. The objective of the method is to reach perfect impulse noise detection and to keep superior visible quality after restoring.
Furthermore, a truncation filter is provided in the prior art. X(i,j) represents a gray scale of a pixel (i,j), and N square windows of an M×M size having the pixel (i,j) can be found. This kind of window is called internal window and is expressed as WIk. For each internal window, a corresponding external window WOk of an (M+2r)×(M+2r) size can be found, wherein r≧1. The internal window WIk and the corresponding external window WOk have the same center. Therefore, N close surrounding bands BK having a thickness r can be found (wherein K=1 . . . N). The close surrounding bands BK are defined as BK=WOK−WIK. Suppose uk and vk represent the maximum gray scale and the minimum gray scale in each close surrounding band BK, the maximum gray scale and the minimum gray scale of its surrounding groups are used for determining whether noise interference is present or not. The objective of this method is protecting image details when reducing noise.
An adaptive two-pass median filtering (ATPMF) is provided in the prior art. Sorting filters such as median filters may result in poor performance when the noise ratio is high. Proceeding with this kind of filters twice will get better performance, which is called two-pass. This method works for two targets: the first, more noise can be reduced by utilizing this two-pass median filtering than a general median filtering when the noise ratio is high; the second, estimated space distribution of impulse noise is utilized to correct errors resulting from the first time filtering. The conception of this method is described in the following:
Step 1: The estimated space distribution and impulse noise value are obtained by utilizing a median filter to reduce image noise.
Step 2: Determining which pixels after reducing noise in step 1 are over-corrected, and using original pixel values to replace these pixels and keeping its value in step 3.
Step 3: Using the median filter to reduce image noise again.
The objective of this method is for being applied in any sorting filters when filtering image that is interfered by high noise ratio.
Thus it can be seen, numerous image noise reduction algorithms are already disclosed in the prior art. In some algorithms, only images that are interfered by high ratio impulse noise are suitable for use. In some conditions, error judgment may happen. Moreover, not only reducing noise effectively but also protecting image details should be a concern.